ADVANCEMENTS IN MULTI-SENSORY SYSTEMS FOR ACCURATE GENDER IDENTIFICATION IN LIVESTOCK

Abstract

Proper determination of gender in livestock is very imperative in enhancing breeding programs, farm management and agricultural productivity. Conventional gender detecting procedures such as manual diagnosis and genetic testing procedures are long-winded, penetrative and frequently a subject to error in these human hands. More recent development of multi-sensory outfits especially those that utilize artificial intelligence (AI) and machine learning (ML) provide a prospective solution that is automated in nature, but focused on the classification of gender. This paper discusses the practice of multi-sensory systems to ensure precise gender recognition in livestock creatures by merging visual, auditory and bio-metric sensors with machine learning algorithms. The paper discusses the former literature and presents a concept of the multi-sensory gender recognition project in livestock. Findings indicate that multimedia classification using sound and biometric characteristics besides the visual representation have better accuracy rates than when individual data are used. As shown in the study, multi-sensory systems that apply AI have the potential to identify the gender of the livestock without invasive interference and do so effectively. Issues to do with sensor integration, data quality, and model interpretability are also outlined as well as the implications the study has on future research.

Keywords: Multi-Sensory Systems, Gender Recognition, Livestock, Artificial Intelligence, Machine Learning, Sensor Fusion, Class Accuracy.